The Kernel Recursive Maximum Total Correntropy Algorithm

Focusing on the performance deterioration of nonlinear filtering algorithms in the error-in-variable (EIV) model, a nonlinear version of maximum total correntropy (MTC) algorithm called kernel recursive maximum total correntropy (KRMTC) is proposed in this paper, which constructs a nonlinear relatio...

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Vydané v:IEEE transactions on circuits and systems. II, Express briefs Ročník 69; číslo 12; s. 1
Hlavní autori: Hou, Xinyan, Zhao, Haiquan, Long, Xiaoqiang, Jin, Weidong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptation models
Adaptive algorithms
Adaptive filters
Algorithms
Approximation algorithms
Convergence
Criteria
Filtering
Filtering algorithms
Kernel
Kernel adaptive filtering
Kernel recursive maximum total correntropy
Kernels
Maximum total correntropy
Performance degradation
Prediction algorithms
Robustness
ISSN:1549-7747, 1558-3791
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Shrnutí:Focusing on the performance deterioration of nonlinear filtering algorithms in the error-in-variable (EIV) model, a nonlinear version of maximum total correntropy (MTC) algorithm called kernel recursive maximum total correntropy (KRMTC) is proposed in this paper, which constructs a nonlinear relationship based on MTC criterion between inputs and outputs in high-dimensional feature space. Compared with existing kernel adaptive filtering (KAF) algorithms based on traditional mean square error (MSE) and maximum correntropy criteria (MCC), the proposed algorithm can handle nonlinear modeling problems in the EIV model well. In addition, an approximate linear dependency (ALD) criterion is applied to reduce the computational complexity of KRMTC. To verify the feasibility of the proposed algorithm, firstly, the convergence of the KRMTC algorithm under some assumptions is discussed and its local convergence condition is given. Secondly, the proposed algorithm is applied to the Lorenz time-series prediction and wind prediction simulations. Simulation results display that the KRMTC algorithm has better performance than kernel recursive least squares (KRLS) and kernel recursive maximum correntropy (KRMC) algorithms in EIV case.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2022.3202800